Psychological well-being (PWB) and depression are important factors that modify risk for Alzheimer's disease (AD), a disorder of progressive erosion of memory and cognition. Specifically, depression is associated with increased risk for AD dementia while PWB with decreased risk for AD after controlling for depression. Molecular mechanisms underlying these important associations, however, are not known and are the focus of this proposal. Based on emerging evidence from our studies and others', we hypothesize that altered expression of key microRNAs (miRNAs) contribute to the effects of depression and PWB on AD risk. To test this hypothesis we propose to study a unique dataset of 850 human postmortem brains from the Rush Memory and Aging Project (MAP). This prospective longitudinal study annually collects data on depression, PWB, cognition, physical health, and dementia, and genomic, transcriptomic, and proteomic data from the dorsolateral prefrontal cortex (dPFC). We propose a 2-stage genetic study to identify key miRNAs, transcripts, and proteins associated with depression, and separately with PWB, and examine how they relate to cognitive change, AD dementia, and dementia-related pathologies. Our discovery dataset will be the 600 MAP samples and replication set will be 250 MAP samples, followed by a joint analysis of all 850. In Aim 1 we propose to identify miRNAs specific to depression and PWB, respectively, through genome-wide miRNA expression analyses. We will then determine how these miRNAs are associated with AD phenotypes (i.e. rate of cognitive decline, clinical diagnosis of dementia, and dementia-related pathologies). We anticipate identifying miRNAs significantly associated with both depression and AD (referred to as Dep-AD- miRNAs), and to both PWB and AD (PWB-AD-miRNAs). In Aim 2, we examine transcript levels of the targets of the Dep-AD-miRNAs and PWB-AD-miRNAs in AD phenotypes. We hypothesize that mRNA levels of these targets will be associated with AD phenotypes. Additionally, we will perform co-expression network analysis on existing transcriptomes to identify expression modules and key expression drivers for depression and PWB, separately. We will then test if these key expression drivers are associated with AD phenotypes. In Aim 3, we will examine protein levels of the downstream targets of the Dep-AD-miRNAs and PWB-AD-miRNAs in AD phenotypes. We also test whether protein levels of the significant transcripts from Aim 2 are associated with AD phenotypes. Lastly, we will perform network analysis on existing global proteomes to identify novel proteomic drivers for depression and PWB, respectively, and examine their association with AD phenotypes. This project can potentially identify important molecular contributors of AD dementia that might not be apparent through other approaches, leading to new insights into mechanisms and treatment targets for AD and thereby have an important and sustained impact on public health.